The present disclosure relates generally to machine vision, and more particularly to machine vision systems that operate by digitizing images that are substantially compressible, i.e. digital images that can be well represented, according to some method of encoding, by a set of numbers that is, at least, 50% smaller than the set of pixel values. For instance, a naturally sparse image, such as the image formed of a scene illuminated by a plane of light is substantially compressible, since the sparse image can be represented by a set of numbers encoding the magnitude and location of a relatively small number of pixels corresponding to points of illumination in the scene, while all other pixel values can be encoded in a single number, which may be zero.
A machine-vision method for capturing information from the surface of an object-of-interest involves a two dimensional (2D) imaging device including a digital camera focused on a plane of illumination provided by a light source arranged at a fixed position relative to the digital camera. By moving the object-of-interest (or the imaging device) in a direction substantially perpendicular to the plane-of-illumination, a three dimensional (3D) point-cloud representing the surface of an object-of-interest may be progressively collected for subsequent analysis. In this method, the speed of 3D point cloud collection is typically limited by the maximum rate at which the digital camera can capture digital images, which may be determined by the design of the digital camera's image sensor.
Conventional complementary metal-oxide semiconductor (CMOS) image sensor architecture has been adapted to provide ultra-high-speed image capture, but the size, the cost, the complexity and the supporting system requirements, generally render such devices impractical for integration in a commercially feasible machine-vision system.